Semantic Scholar FastMCP Server is designed to streamline academic research by exposing a RESTful API that sits between your application and the Semantic Scholar database. It orchestrates multiple prompt chains (MCP) in parallel—such as metadata retrieval, abstract summarization, citation extraction, and question answering—to produce fully processed results in a single response. Developers can configure each chain’s parameters, swap out language models, or add custom handlers, enabling rapid deployment of literature review assistants, research chatbots, and domain-specific knowledge pipelines without building complex orchestration logic from scratch.

Stellr is a state-of-the-art electronic lab notebook (ELN) developed to enhance documentation and data management for scientists in the bio-pharma industry. This digital tool helps researchers and lab workers transition from outdated paper-based methods to a more efficient, accessible, and centralized platform. Stellr centralizes all your laboratory data, making it readily searchable and easily shareable within your team. The platform's user-friendly interface simplifies the process of documenting experiments and managing lab results, driving productivity and ensuring compliance with industry standards.

ChatDox is an innovative platform designed to transform how users interact with texts. By enabling conversational interfaces with documents such as books, papers, manuals, and contracts, ChatDox offers an intuitive and seamless experience. Users can ask questions and receive instant information, making it an invaluable tool for researchers, professionals, and students alike. The platform simplifies and accelerates the process of finding relevant information, thus saving time and improving productivity.

AgenticIR (Agentic Information Retrieval) provides a modular framework where LLM-powered agents autonomously plan and execute IR workflows. It enables the definition of agent roles — such as query generator, document retriever, and summarizer — running in customizable sequences. Agents can fetch raw text, refine queries based on intermediate results, and merge extracted passages into concise summaries. The framework supports multi-step pipelines including iterative web search, API-based data ingestion, and local document parsing. Developers can adjust agent parameters, plug in different LLMs, and fine-tune behavior policies. AgenticIR also offers logging, error handling, and parallel agent execution to accelerate large-scale information gathering. With a minimal code setup, researchers and engineers can prototype and deploy autonomous retrieval systems.